Unit 1 Walkthrough: Peer Interaction & MOOC-Eds

ECI 589 Social Network Analysis and Education

1. PREPARE

During the second week of each unit, we’ll “walk through” a basic research workflow, or data analysis process, modeled after the Data-Intensive Research Workflow from Learning Analytics Goes to School (Krumm et al., 2018):

Each walkthrough will focus on a basic analysis guided by the social network perspective. This week, our focus will be on preparing relational data for analysis, putting together some basic network descriptives, and plotting a network visualization to help illustrate key findings.

Specifically, the Unit 1 Walkthrough will cover the following topics:

1. Prepare: Prior to analysis, we’ll take a look at the context from which our data came, formulate some research questions, and get introduced the {igraph} R package for network analysis.

2. Wrangle: Wrangling data entails the work of manipulating, cleaning, transforming, and merging data. In section 2 we focus on importing network data, converting our familiar data frames into a network object that can be analyzed and graphed, and learn about “simple graphs.”

3. Explore: In section 3, we calculate some basic network descriptives and learn how to summarize the stats with a sociogram.

4. Model: While we won’t dig into approaches for modeling network data Unit 3, we will take a quick look at some approaches used in the study guiding this walkthrough.

5. Communicate: We’ll learn more about communicating key findings next week, but for now will be introduced to the basic components of a data product.

1a. Review the Research

Prior to analysis, it’s critical to understand the context and data sources available so you can formulate useful questions that can be feasibly addressed by your data. In Social Network Analysis and Education: Theory, Methods & Applications, Carolyn (2013) notes that:

the social network perspective is one concerned with the structure of relations and the implication this structure has on individual or group behavior and attitudes

More specifically, Carolyn cites the following four features used by Freeman (2004) to define the social network perspective:

1. Social network analysis is motivated by a relational intuition based on ties connecting social actors.

2. It is firmly grounded in systematic empirical data.

3. It makes use of graphic imagery to represent actors and their relations with one another.

4. It relies on mathematical and/or computational models to succinctly represent the complexity of social life.

For Unit 1, our walkthrough will be guided by previous research and evaluation work conducted by the Friday Institute for Educational Innovation as part of the Massively Open Online Courses for Educators (MOOC-Ed) initiative.

A Social Network Perspective in MOOC-Eds

Kellogg, S., Booth, S., & Oliver, K. (2014). A social network perspective on peer supported learning in MOOCs for educators. International Review of Research in Open and Distributed Learning, 15(5), 263-289.

Research Context

In the spring of 2013, The Friday Institute launched the MOOC-Ed Initiative to explore the potential of delivering personalized, high-quality professional development to educators at scale (Kleiman et al., 2013). In collaboration with the Alliance for Excellent Education, the Friday Institute launched this initiative with a 6-week pilot course called Planning for the Digital Learning Transition in K-12 Schools (DLT 1), which was offered again in September 2013 (DLT 2). This course was designed to help school and district leaders plan and implement K-12 digital learning initiatives.

Academics, as well as pundits from traditional and new media, have raised a number of concerns about MOOCs, including the lack of instructional and social supports. Among the core design principles of MOOC-Eds are collaboration and peer-supported learning. It is an assumption of this study that challenges arising form this problem of scale can be addressed by leveraging these massive numbers to develop robust online learning communities.

This mixed-methods case study used both SNA and qualitative methods to better understand peer support in MOOC-Eds through an examination of the characteristics, mechanisms, and outcomes of peer networks. Findings from this study demonstrate that even with technology as basic as a discussion forum, MOOCs can be leveraged to foster these networks and facilitate peer-supported learning. Although this study was limited to two unique cases along the wide spectrum of MOOCs, the methods applied provide other researchers with an approach for better understanding the dynamic process of peer supported learning in MOOCs.

Data Sources

MOOC-Ed registration form. All participants completed a registration form for each MOOC-Ed course. The registration form consists of self-reported demographic data, including information related to their professional role and work setting, years of experience in education, and personal learning goals.

MOOC-Ed discussion forums. All peer interaction, including peer discussion, feedback, and reactions (e.g., likes), take place within the forum area of MOOC-Eds, which are powered by Vanilla Forums. Because of the specific focus on peer supported learning, postings to or from course facilitators and staff were removed from the data set. Finally, analyses described below exclude more passive forms of interactions (i.e., read and reaction logs), and include only postings among peers.

For our Unit 1 walkthrough, we’ll take a look at data from the original Digital Learning Transition in K-12 Schools (DLT 1) that was not included in this study to allow for comparisons to the findings in this study. Also, for your independent analysis, you may want to consider working with the DLT 2 data to see if you can replicate some of the findings from this paper!

👉 Your Turn ⤵

Take a quick look at the Description of the Dataset section from the Massively Open Online Course for Educators (MOOC-Ed) network dataset BJET article and the accompanying data sets stored on Harvard Dataverse that we’ll be using for this walkthrough.

In the space below, type a brief response to the following questions:

1. What were some of the steps necessary to construct this dataset?

2. What two “node attributes” from the dataset that might be useful for predicting participants who may be more engaged or central to the network? Why did you select those two?

3. What else do you notice/wonder about this dataset?

1b. Identify a Question(s)

A Social Network Perspective on Peer Supported Learning in MOOC-Eds was framed by three primary research questions related to peer supported learning:

1. What are the patterns of peer interaction and the structure of peer networks that emerge over the course of a MOOC-Ed?

2. To what extent do participant and network attributes (e.g., homophily, reciprocity, transitivity) account for the structure of these networks?

3. To what extent do these networks result in the co-construction of new knowledge?

For our very first walkthrough, we are going to focus exclusively on RQ1 from the original study and our question of interest about our educator network is:

To what extent, did educators engage with other participants in the discussion forums?

👉 Your Turn ⤵

Based on what you know about networks and the context so far, what other research questions might ask we ask in this context that a social network perspective might be able to answer?

In the space below, type a brief response to the following questions:

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We’ll revisit your response towards the end and provide an opportunity to refine your research question after you know the data a little better.

1c. Load Libraries

As highlighted in Chapter 6 of Data Science in Education Using R (DSIEUR):

Packages are shareable collections of R code that can contain functions, data, and/or documentation. Packages increase the functionality of R by providing access to additional functions to suit a variety of needs.

Let’s check to see which packages have already been loaded into our RStudio Cloud workspace. Take a look at the the Files, Plots, & Packages Pane in the lower right hand corner of RStudio Cloud to make sure these packages have been installed and loaded:

You should see some familiar tidytext packages from our Getting Started Walkthrough like {dplyr} and {readr} which we’ll be using again shortly. You should also see an important package call {igraph} that will rely on heavily for our network analyses.

If you are working in RStudio Desktop, or notice that the packages have not been installed and/or loaded, run the following install.packages() function code to install the {tidyverse} and {igraph} packages:

install.packages("tidyverse")
install.packages("igraph") 

Let’s go ahead and use library() function for the {tidyverse} package just to review the other packages from the tidyverse collection of packages that this package contains:

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.3     ✓ purrr   0.3.4
✓ tibble  3.1.2     ✓ dplyr   1.0.6
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

🎉 igraph Package 📦

For our Unit 1 Walkthrough, we will rely heavily on the igraph network analysis package. The main goals of the igraph package and the collection of network analysis tools it contains are to provide a set of data types and functions for:

1. pain-free implementation of graph algorithms,

2. fast handling of large graphs, with millions of vertices and edges.

3. allowing rapid prototyping via high level languages like R.

Run the code chunk below to load the {igraph] library:

library(igraph)

Attaching package: ‘igraph’

The following objects are masked from ‘package:dplyr’:

    as_data_frame, groups, union

The following objects are masked from ‘package:purrr’:

    compose, simplify

The following object is masked from ‘package:tidyr’:

    crossing

The following object is masked from ‘package:tibble’:

    as_data_frame

The following objects are masked from ‘package:stats’:

    decompose, spectrum

The following object is masked from ‘package:base’:

    union

👉 Your Turn ⤵

Take a look at the messages from the output of after loading the igraph library. What tidyverse packages share identically named functions with igraph?

Write your response in the space below.

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🧶 Knit & Check ✅

Congrats! You made it to the end of data wrangling section and are ready to start analysis! Before proceeding further, knit your document and check to see if you encounter any errors.

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2. WRANGLE

In general, data wrangling involves some combination of cleaning, reshaping, transforming, and merging data (Wickham & Grolemund, 2017). The importance of data wrangling is difficult to overstate, as it involves the initial steps of going from the raw data to a dataset that can be explored and modeled (Krumm et al, 2018).

For our data wrangling this week, we’re keeping it simple since working with network data is a bit of a departure from our working with rectangular data frames. Our primary goals for Unit 1 are learning how to:

1. Import Data. Before working with data, we need to “read” it into R and once imported, we’ll take at different ways to view our data in R.

2. Create a Network Object.

3. Simplify Network. Finally, we’ll learn about a handy simplify() function in the {igraph} package for removing ties that .

2a. Import Data

The Edge-List Format

To get started, we need to import, or “read”, our data into R. The function used to import your data will depend on the file format of the data you are trying to import, but R is pretty adept at working with many files types.

Take a look in the /data folder in your Files pane. You should see the following .csv files:

• dlt1-edgelist.csv

• dlt1-nodes.csv

As its name implies, the first file dlt1-edgelist.csv is an edge-list that contains information about each tie, or relation between two actors in a network. In this context, a “tie” is a reply by one participant in the discussion forum to the post of another participant - or in some cases to their own post! These ties between a single actor are called “self-loops” and as we’ll see later in this section, igraph has a special function to remove these self loops from a sociogram, or network visualization.

The edge-list format is slightly different than other formats you have likely worked with before in that the values in the first two columns each row represent a dyad, or tie between two nodes in a network. An edge-list can also contain other information regarding the strength, duration, or frequency of the relationship, sometime called “weight”, in addition to other “edge attributes.”

In addition to

• Sender = Unique identifier of author of comment

• Receiver = Unique identifier of identified recipient of comment

• Timestamp = Time comment was posted

• Parent = Primary category or topic of thread

• Category = Subcategory or subtopic of thread

• Thread_id = Unique identifier of a thread

• Comment_id = Unique identifier of a comment\

Let’s use the read_csv() function from the {readr} package introduced in the Getting Started walkthrough to read in our edge-list and print the new ties data frame:

ties <- read_csv("data/dlt1-edgelist.csv", 
                 col_types = cols(Sender = col_character(), 
                                  Receiver = col_character(), 
                                  `Category Text` = col_skip(), 
                                  `Comment ID` = col_character(), 
                                  `Discussion ID` = col_character()))

ties

Note the addition of the col_types = argument for changing the column types to character strings since the numbers for for those particular columns indicate actors (Sender and Reciever) and attributes (Comment_ID and Discussion_Id). We also skipped the Category Text.

RStudio Tip: Importing data and dealing with data types can be a bit tricky, especially for beginners. Fortunately, RStudio has an “Import Dataset” feature in the Environment Pane that can help you use the {readr} package and associated functions to greatly facilitate this process.

👉 Your Turn ⤵

Consider the example pictured below of a discussion thread from the Planning for the Digital Learning Transition in K-12 Schools (DLT 1) where our data orginated. This thread was initiated by participant I, so the comments by J and N are considered to be directed at I. The comment of B, however, is a direct response to the comment by N as signaled by the use of the quote-feature as well as the explicit mentioning of N’s name within B’s comment.

Now answer the following questions as they relate to the DLT 1 edge-list we just read into R.

1. Which actors in this thread are the Sender and the Reciever? Which actor is both?

2. How many dyads are in this thread? Which pairs of actors are dyads?

Sidebar: Unfortunately, these types of nuances in discussion forum data as illustrated by this simple example are rarely captured through automated approaches to constructing networks. Fortunately, the dataset you are working with was carefully reviewed to try and capture more accurately the intended recipients of each reply.

Node Attributes

The second file is we’ll be using to help understand out network and the actors involved contains all the nodes or actors (i.e., participants who posted to the discussion forum) as well as some of their attributes such as gender and years of experience in education.

Carolyn (2013) notes that most social network analyses include variables that describe attributes of actors, ones that are either categorical (e.g., sex, race, etc.) or continuous in nature (e.g., test scores, number of times absent, etc.). These attributes that can be incorporated into a network graph or model to making it much more informative and can aid in testing or generating hypotheses.

These attribute variables are typically included in a rectangular array, or dataframe, that mimics the actor-by-attribute that is the dominant convention in social science, i.e. rows represent cases, columns represent variables, and cells consist of values on those variables.

As and aside, Carolyn also refers to this historical preference by researchers for “actor-by-attribute” data, in the absence of relational data in which the actor has been removed their social context, as the “sociological meatgrinder” in action. Specifically, this historical approach assumes that the actor does not interact with anyone else in the study and that outcomes are solely dependent of the characteristics of the individual.

Regardless, let’s read in our node attribute file and take a look at the actors and their attributes included in our dataset:

actors <- read_csv("data/dlt1-nodes.csv", 
                   col_types = cols(UID = col_character(), 
                                    Facilitator = col_character(), 
                                    expert = col_character(), 
                                    connect = col_character()))

👉 Your Turn ⤵

Use the code chunk below and a function of your choosing to take a look at the actors data frame:

Match up the attributes included in the node file with the following codebook descriptors. The first one has been done as an example.

• Facilitator = Identification of course facilitator (1 = instructor)
• Dummy variable for whether participants listed networking/collaboration with others as one of their course goals on the registration form
• Identifier of “expert panelists” invited to course to share experience through recorded Q&A
• Identification of course facilitator (1 = instructor)
• Professional role (eg, teacher, librarian, administrator)
• Years of experience as an educator
• Works with elementary, middle, and/or high school students
• Initial assignment of discussion group

2b. Create Network Object

Before we can begin using many of the functions from the {igraph} for summarizing and visualizing our DLT 1 network, we first need to convert the data frames that we imported into an igraph network object, or an igraph graph.

Convert to igraph Graph

To do that, we will use the graph_from_data_frame() function. Note that I included the eval=FALSE argument in the code block below to prevent this code from running when we knit our final document. Otherwise it will produce an error since we can’t include help documentation in our knitted HTML file.

Run the following code to take a look at the help documentation for this function:

?graph_from_data_frame

You probably saw that this particular function takes the following three arguments, two of which are data frames:

• d describes the edges of the network. The first two columns are the IDs of the source and the target node for each edge, in our case the Sender and Reviever of a discussion post. The order matters! The following columns are edge attributes such as weight, type, label, or anything else.

• vertices starts with a column of node IDs and any following columns are interpreted as node attributes.

• directed is determines whether or not to create a directed graph.

Run the following code to specify our ties data frame as the edges of our network, our actors data frame for the vertices of our network and their attributes, and indicate that this is indeed a directed network.

network <- graph_from_data_frame(d = ties, 
                                 vertices = actors, 
                                 directed = T) 

network

IGRAPH b92ec87 DN-- 445 2529 -- 
+ attr: name (v/c), Facilitator (v/c), role1 (v/c), experience (v/n), experience2
| (v/c), grades (v/c), location (v/c), region (v/c), country (v/c), group (v/c),
| gender (v/c), expert (v/c), connect (v/c), Timestamp (e/c), Discussion Title (e/c),
| Discussion Category (e/c), Parent Category (e/c), Discussion Identifier (e/c),
| Comment ID (e/c), Discussion ID (e/c)
+ edges from b92ec87 (vertex names):
 [1] 360->444 356->444 356->444 344->444 392->444 219->444 318->444 4  ->444 355->356 355->444
[11] 4  ->444 310->444 248->444 150->444 19 ->310 216->19  19 ->444 19 ->4   217->310 385->444
[21] 217->444 393->444 217->19  256->219 253->444 301->444 301->444 143->444 218->19  361->217
[31] 30 ->444 30 ->444 335->444 166->444 156->219 173->444 223->444 219->19  219->253 261->444
+ ... omitted several edges

Carolyn (2013) reminds us that one of the simplest and often ignored structural property of a social network is its size and explains that:

size is simply a measure of the number of nodes in the network.

He notes that the size of a network plays an important role in determining what happens in the network. For example, in a classroom of 30 students, it is not hard to imagine that the pattern of who communicates with whom will look much different than if the network consisted of hundreds or even thousands of students like in a MOOC.

👉 Your Turn ⤵

Take a look at the very first line of the output which contains some basic information about our network and answer the following questions:

1. How many notes and edges are in our network? Is this consistent with the number of observations in our data frames? Hint: Check the Environment pane.

2. The “D” and the “N” indicate that this is a Directed network and has the Name vertex attributes set. Why do the two spaces that follow these letters have dashes? Hint: check the help files.

3. Which vertex attribute did igraph interpret as numeric?

Simplify Graph

As you saw from the network output, our dataset has 2529 edges or ties and just a quick scan of the edges in the network shows that edges like 356 -> 444 occur at least more than once. So we know that participant 356 has replied to participant 444 at least twice.

Fortunately, the {igraph} package has a simplify() function for collapsing these duplicate edges so they are not represented more than once when we want visually depict our network with a sociogram.

Let’s use that function to simplify our network and save it as a simple_network, or a simple graph, which contains no self-loops or duplicate edges and which by default the simplify() function removes:

simple_network <- simplify(network)

simple_network

👉 Your Turn ⤵

Take a look at the output for our simple graph now and answer the following questions:

1. How many unique edges are in the network?

2. Did we lose any important or potentially useful information by collapsing multiple edges into a single edge?

Add Edge Weights

We noted earlier that edges can also contain attributes such as strength, duration or frequency, sometime called “weight.” These weights can not only help us better understand the relationship between two actors, but also aid in visualization and modeling later on.

When we used the simplify() function earlier, it collapsed our duplicate edges but we lost some vital information as a result, namely the frequency of replies among pairs of educators in our discussion forum.

Fortunately, the simplify() function contains an argument that will allow us to count the number of ties between two actors, similar to how we might use the count() function in the {dplyr} package like so:

edge_weights <- count(ties, Sender, Receiver)

edge_weights

In this case, we see that participant 1 replied to participant 144 twice throughout the course.

To add weights to our simplified network, we first need to add a weight variable to the edges in our original network igraph object.

The {igraph} package has a unique syntax for working with attributes of network objects. To add a weight attribute to the E() edges in our network we’ll use the $ operator which can be used to create a new weight variable – or select a variable as we’ll see later on – and use the <- assignment operator to add an initial value of 1 for the weight of each edge.

Let’s put that all together and run the code to add a weight of 1 to each edge in our network

E(network)$weight <- 1  

Now let’s take a look at our igraph network object again:

network

We can see that our network is now weighted as indicated by the “W” and that our new weight attribute has been added.

weighted_network <- simplify(network,
                           edge.attr.comb = list(weight="sum")
                           )

weighted_network

👉 Your Turn ⤵

🧶 Knit & Check ✅

Congrats! You made it to the end of data wrangling section and are ready to start analysis! Before proceeding further, knit your document and check to see if you encounter any errors.

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3. EXPLORE

As highlighted in both DSEIUR and Learning Analytics Goes to School, calculating summary statistics, data visualization, and feature engineering (the process of creating new variables from a dataset) are a key part of exploratory data analysis. One goal in this phase is explore questions that drove the original analysis and develop new questions and hypotheses to test in later stages.

In Section 3, we will learn some new functions for retrieving basic network descriptives related to our research question and create a network visualization to help illustrate key findings. Specifically, this section we’ll learn to:

1. Examine Basic Descriptives. We focus primarily on actors and edges in this walkthrough, including the edges wights we added in the previous section as well as node degree, and import and fairly intuitive measure of centrality.

2. Make a Sociogram. Finally, we wrap up the explore phases by learning to plot a network and tweak key elements like the size, shape, and position of nodes and edges to better at communicating key findings.

3a. Examine Basic Descriptives

Edge Weights

mean(edge_weights$n)
median(edge_weights$n)
hist(edge_weights$n, breaks = 10)

Now that we’ve looked at the typical frequency of those

Node Degree

A key structural property of networks is the concept of centralization. A network that is highly centralized is one in which relations are focused on a small number of actors or even a single actor in a network, whereas ties in a decentralized network are diffuse and spread over a number of actors. One of the most common measure of centralization is degree

Degree is the number of ties to and from an ego. In a directed network, in-degree is the number of ties received, whereas out-degree is the number of ties sent.

The {igraph} package has an aptly named function degree() for retrieving degree, in-degree, and out-degree for all actors in a network.

Run the following code to extract measures and save to node_degree which we’ll examine in just a bit:

node_degree <- degree(weighted_network, mode="all")

Note that we set the mode = argument in this function to “all” which give us the total number of participants that each actor in our network with sent or received a reply.

Let’s take a look at the distribution of node_degree in our network by using R’s built in hist() function for creating histograms. I set the value of breaks =, or bins in our histogram, to 30 since I already know some actors in this network have a very high degree.

hist(node_degree, breaks = 30)

We can see that most actors in the network are connected to very few individuals while a couple actors in this network are connected to a very larger number of alters.

Now let’s take a look at the mean and median for node_degree using some other {base} R functions:

mean(node_degree)

[1] 8.701124

median(node_degree)

[1] 4

We see that the mean suggests the participants are, on average, connected to about 8 other participants in the MOOC-Ed, but this is likely heavily skewed by the two individuals with a disproportionate number of connections. The median is probably a better characterization of the typical number of educators a participant has sent or received a reply.

In-Degree

Let’s go ahead and take a look at in-degree next:

in_degree <- degree(weighted_network, mode="in")

hist(in_degree, breaks = 30)

mean(in_degree)

[1] 4.350562

median(in_degree)

[1] 1

👉 Your Turn ⤵

Use the code chunk

In the space below, write your interpretation of these results.

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3b. Make a Sociogram

igraph arguments only

plot(weighted_network)

Hair ball

Remove Labels

plot(weighted_network,
     vertex.label = NA)

Change vertex size

plot(weighted_network,
     vertex.label = NA,
     vertex.size = 1)

degree

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree)

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree*.1)

Reduce arrow size

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree*.1,
     edge.arrow.size = .04)

Adjust Edge Width

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree*.05,
     edge.arrow.size = .04,
     edge.width = .2)

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree*.1,
     edge.arrow.size = .04,
     edge.width = E(weighted_network)$weight)

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree*.05,
     edge.arrow.size = .05,
     edge.width = E(weighted_network)$weight/5)

Change Layout

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree*.05,
     edge.arrow.size = .05,
     edge.width = E(weighted_network)$weight/5,
     layout = layout_with_fr)

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree*.05,
     edge.arrow.size = .05,
     edge.width = E(weighted_network)$weight/5,
     layout = layout_with_kk)

plot(weighted_network,
     vertex.label = NA,
     vertex.size = node_degree*.05,
     edge.arrow.size = .05,
     edge.width = E(weighted_network)$weight/5,
     layout = layout_in_circle)

👉 Your Turn ⤵

We’ve only scratched the surface of what’s possible with network plotting but hopefully this have given you a sense of

🧶 Knit & Check ✅

Congrats! You made it to the end of the Explore section and are ready to learn a little about network modeling! Before proceeding further, knit your document and check to see if you encounter any errors.

4. MODEL

As highlighted in Chapter 3 of Data Science in Education Using R, the Model step of the data science process entails “using statistical models, from simple to complex, to understand trends and patterns in the data.” The authors note that while descriptive statistics and data visualization during the Explore step can help us to identify patterns and relationships in our data, statistical models can be used to help us determine if relationships, patterns and trends are actually meaningful.

We will not explore the use of models for SNA until Unit 3, recall from the PREPARE section that the Kellogg et al. study was guided by the following questions:

1. What are the patterns of peer interaction and the structure of peer networks that emerge over the course of a MOOC-Ed?

2. To what extent do participant and network attributes (e.g., homophily, reciprocity, transitivity) account for the structure of these networks?

3. To what extent do these networks result in the co-construction of new knowledge?

To address Question 1, actors in the network were categorized into distinct mutually exclusive groups using the core-periphery and regular equivalence functions of UCINET. The former used the CORR algorithm to divide the network into actors that are part of a densely connected subgroup, or “core”, from those that are part of the sparsely connected periphery. Regular equivalence employs the REGE blockmodeling algorithm to partition, or group, actors in the network based on the similarity of their ties to others with similar ties. In essence, blockmodeling provides a systematic way for categorizing educators based on the ways in which they interacted with peers.

As we saw upon just a basic visual inspection of our network during the Explore section, there was a small core of highly connected participants surrounded by those on the “periphery,” or edge, of the network with very few connections. In the DLT 2 course, those on the periphery made up roughly 90% of network. The study also found relatively high levels of reciprocation, but also found that roughly a quarter of participants were characterized as “brodcasters” – educators who initiated a discussion thread, but neither reciprocated with those who replied, nor posted to threads initiated by others.

To address Question 2, this study use the exponential family of random graph models (ERGM; also known as p* models), which provide a statistical approach to network modeling that addresses the complex dependencies within networks. ERGMs predict network ties and determine the statistical likelihood of a given network structure, based on an assumed dependency structure, the attributes of the individuals (e.g., gender, popularity, location, previous ties) and prior states of the network.

👉 Your Turn ⤵

Recall from the 1a. Review the Research that you were asked to identify two “node attributes” from the dataset that might be useful for predicting participants who may be more engaged or central to the network.

Take look at page 276 of the article, A social network perspective on peer supported learning in MOOCs for educators. Were your predictions correct?

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5. COMMUNICATE

The final step in our workflow/process is sharing the results of analysis with wider audience. Krumm et al. (2018) have outline the following 3-step process for communicating with education stakeholders what you have learned through analysis:

1. Select. Communicating what one has learned involves selecting among those analyses that are most important and most useful to an intended audience, as well as selecting a form for displaying that information, such as a graph or table in static or interactive form, i.e. a “data product.”

2. Polish. After creating initial versions of data products, research teams often spend time refining or polishing them, by adding or editing titles, labels, and notations and by working with colors and shapes to highlight key points.

3. Narrate. Writing a narrative to accompany the data products involves, at a minimum, pairing a data product with its related research question, describing how best to interpret the data product, and explaining the ways in which the data product helps answer the research question.

Next week we’ll take a look at refining our analysis and ways we might communicate and share findings with education stakeholders.

👉 Your Turn ⤵

Now that you’ve become more familiar with this dataset and the social network perspective, what other aspects of this dataset, or a dataset you are interested in exploring, could you investigate?

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What specific research questions might you ask that would be helpful for being understanding and improving learning, or the context in which the data is collected?

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🧶 Knit & Check ✅

Congrats! You’ve finished the Unit 1 Guided Walkthrough and are ready for some independent analysis next week!

To complete this assignment, knit your document and send me an email at sbkellog@ncsu.edu letting me know you’re all set.